license: mit
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- HTS
pretty_name: Assay-Interfering-Compounds Finder
size_categories:
- 1M<n<10M
Boldini2024 (Assay-Interfering-Compounds Finder)
17 Datasets that are used to employ Minimum Variance Sampling Analysis (MVS-A) to find Assay Interfering Compounds (AIC) in High Throughput Screening data. In this study, they present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. Their method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign.
The datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset. If you would like to try these processes with the original dataset, please follow the instructions in the Processing Script.py file located in the maomlab/Boldini2024.
Citation
ACS Cent. Sci. 2024, 10, 4, 823–832 Publication Date:March 15, 2024 https://doi.org/10.1021/acscentsci.3c01517